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Each year KMWorld identifies a host of technologies that we deem "trend-setting" in the broad sphere of knowledge management. Some of the technologies fall into such "hot" categories as artificial intelligence, machine learning and the Internet of Things. Others are more tried-and-true, providing increasing functionality in enterprise collaboration and information management, text analytics and search, compliance and information governance, customer service and support, business process management and case management.

Delving into customer thoughts: TEXT ANALYTICS provides insights

Text analytics is a powerful tool that so far has been used most extensively for analyzing customer-related information. “The tools are business-friendly and the content—once parsed—is easy to process,” says Leslie Owens, VP of research at Forrester. “The leading vendors have found the most adoption in social listening and are well established, with robust cloud-based solutions in this area.” Other applications for text analytics are also growing, and predictions for the worldwide market indicate that it will increase from under $2 billion at present to nearly $5 billion by 2019.

Developing strategies

Oberweis Dairy evolved from a family dairy business based in Illinois. Founded in the early 1900s, Oberweis still offers home delivery of milk and ice cream in the Midwest and in several other states throughout the country. In addition, the company operates about 50 retail dairy stores that sell its products, and recently opened several restaurants that are co-located with its stores.

To provide detailed insights into its operations, Oberweis decided about five years ago to deploy a business intelligence (BI) solution. Prior to that, the company had used consulting services for some of its analytics support, but wanted to bring that function completely in house. After considering a variety of analytic tools, Oberweis selected the SAS analytics platform. One of the early uses of SAS was to provide analyses that would help develop more effective marketing strategies.

In addition to carrying out numerous quantitative analyses to support that goal, Oberweis also began using SAS Text Miner to analyze notes taken by the customer service representatives in its call center. “Our home delivery business had begun to slow down,” says Bruce Bedford, VP of marketing analytics and consumer insight at Oberweis, “and we did not have a handle on the attrition rate by segment.” Oberweis decided to explore the issue by analyzing the content in its customer relationship management (CRM) system.

Using Text Miner helped identify one particular promotional strategy—six months of free delivery—that was correlated with a higher attrition rate. “When the promotional phase was over, customers called in to object to having a new charge on their bills,” recalls Bedford, “and sometimes had even forgotten that the offer was temporary.” Information from the CRM system revealed details about the customers’ dissatisfaction. As a result, Oberweis modified the offer to be in the form of a discount on delivery. “Even though this change was very slight, it was effective,” Bedford says. “The customers no longer experienced such a noticeable discontinuity in the nature of their service, and retention was improved.”

The CSR resource

The ongoing process of text analytics also provides some serendipitous insights. “We use customer complaints as an indicator for how we can improve our products and services,” Bedford says. “At one point, customers were complaining about the milk in a variety of ways, such as ‘cream on top,’ or ‘milk looks funny.’ SAS helped us group the comments despite the different terminology that the customers were using.” The graphics that present a visualization of the text data generated a new category and a spike. “We discovered that one batch of milk had been produced that was not homogenized. Once an adjustment in a machine setting was made, the problem was solved,” explains Bedford.

Over time, it became evident to Oberweis that the customer service representatives were a resource that could be used for more than customer service. “We realized that the call center could also be the primary point of contact when products were launched,” says Bedford. Oberweis identified a goal for each representative that related to cross-selling or upselling, and tied it to the employee’s bonus program. “The reps were able to use the CRM system to document the products sold and their value,” he continues. “This approach was very useful because the reps did not have to change the way they worked, yet we could capture the new information. The information is fed into the SAS repository for analysis.”

An interesting bonus was that the customer service representatives, now renamed marketing representatives, have become more engaged and are serving as the vanguard for new product introductions. “The outcome has been very positive,” Bedford says, “and we were able to create this change without having to re-engineer our proprietary database to accommodate the new information. Although we are a small company, SAS has really helped us function as a large one by allowing us to use powerful analytics to leverage our information more effectively.”

Analytics, applications and adjustments

The use of text analytics for customer-related information has been so dominant that its use for applications tends to be overshadowed, but those also are continuing to grow. Another relatively common use of text analytics is for fraud detection. “One European government was able to reduce tax-related fraud by 98 percent simply by noting the contents of certain fields on a form,” says Fiona McNeill, global product marketing manager at SAS. “Others are building knowledgebases by consolidating service notes into a searchable format and analyzing the contents.”

Root cause analysis relies on text analytics to understand scenarios in which manufactured parts are failing. “The text related to insurance claims and servicing logs associated with manufacturing can help pinpoint the underlying causes of failure in a way that quantitative data alone cannot,” McNeill adds.